Artificial Intelligence in Vascular Neurology: Applications, Challenges, and a Review of AI Tools for Stroke Imaging, Clinical Decision Making, and Outcome Prediction Models.
Authors
Affiliations (2)
Affiliations (2)
- Department of Surgery, College of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA.
- Department of Neurology, Loyola University Health System, Maywood, IL, 60153, USA. [email protected].
Abstract
Artificial intelligence (AI) promises to compress stroke treatment timelines, yet its clinical return on investment remains uncertain. We interrogate state‑of‑the‑art AI platforms across imaging, workflow orchestration, and outcome prediction to clarify value drivers and execution risks. Convolutional, recurrent, and transformer architectures now trigger large‑vessel‑occlusion alerts, delineate ischemic core in seconds, and forecast 90‑day function. Commercial deployments-RapidAI, Viz.ai, Aidoc-report double‑digit reductions in door‑to‑needle metrics and expanded thrombectomy eligibility. However, dataset bias, opaque reasoning, and limited external validation constrain scalability. Hybrid image‑plus‑clinical models elevate predictive accuracy but intensify data‑governance demands. AI can operationalize precision stroke care, but enterprise‑grade adoption requires federated data pipelines, explainable‑AI dashboards, and fit‑for‑purpose regulation. Prospective multicenter trials and continuous lifecycle surveillance are mandatory to convert algorithmic promise into reproducible, equitable patient benefit.